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<p>Ovarian cancer (OC) is a leading cause of gynecologic cancer mortality, with survival prediction limited by existing prognostic models that fail to capture tumor heterogeneity. Conventional methods lack precision for individualized risk assessment. Deep learning (DL) addresses these issues by integrating diverse data to improve survival prediction and risk stratification. This study introduces OvcaSurvivor, a novel multimodal DL framework for R0-resected OC patients, integrating whole-slide images (WSI), ultrasound (US), and clinical data from 543 patients. It uses advanced neural networks (CHIEF for WSI, ResNet50 for US) and an attention-guided fusion module. OvcaSurvivor showed superior performance, with C-indices of 0.81 (internal), 0.76 (external 1), and 0.70 (external 2). Time-dependent AUCs for 1-, 3-, and 5-year survival were highly accurate. WSI features drove prediction, and the model stratified patients into high/low-risk groups, highlighting clinical utility. This multimodal fusion advances OC precision oncology, enabling robust postoperative management.</p>

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Survival prediction and risk stratification in R0-resected ovarian cancer: a multi-modal deep learning approach

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年1月10日

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  • Yi Zhou,
  • Yayang Duan,
  • Mingda Teng,
  • Siyao Li,
  • Haonan Zhang,
  • Fang He,
  • Chuanfen Gao,
  • Yun Xiong,
  • Junli Wang,
  • Xiangshan Fan,
  • Chaoxue Zhang

摘要

Ovarian cancer (OC) is a leading cause of gynecologic cancer mortality, with survival prediction limited by existing prognostic models that fail to capture tumor heterogeneity. Conventional methods lack precision for individualized risk assessment. Deep learning (DL) addresses these issues by integrating diverse data to improve survival prediction and risk stratification. This study introduces OvcaSurvivor, a novel multimodal DL framework for R0-resected OC patients, integrating whole-slide images (WSI), ultrasound (US), and clinical data from 543 patients. It uses advanced neural networks (CHIEF for WSI, ResNet50 for US) and an attention-guided fusion module. OvcaSurvivor showed superior performance, with C-indices of 0.81 (internal), 0.76 (external 1), and 0.70 (external 2). Time-dependent AUCs for 1-, 3-, and 5-year survival were highly accurate. WSI features drove prediction, and the model stratified patients into high/low-risk groups, highlighting clinical utility. This multimodal fusion advances OC precision oncology, enabling robust postoperative management.